MassIVE.quant: a curated and scalable community resource for quantitative proteomics

MassIVE.quant：定量蛋白质组学的精选且可扩展的社区资源

• 批准号: 9764743
• 负责人: Nuno Bandeira
• 金额: $ 34.16万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9764743
• 关键词: Area; Benchmarking; Biological; Biological Process; Biomedical Research; Collection; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Evaluation; Experimental Designs; FAIR principles; Individual; Infrastructure; Investigation; Journals; Link; Manuals; Mass Spectrum Analysis; Metadata; Molecular; Peptides; Procedures; Proteins; Proteome; Proteomics; Protocols documentation; Reproducibility; Research; Research Methodology; Research Personnel; Research Support; Resources; Sampling; Scientist; Standardization; Statistical Methods; Structure; Training; base; computerized data processing; data acquisition; data resource; design; experimental study; improved; innovation; insight; interest; novel; open source; outreach; peer; preservation; repository; tool; virtual reality

PROJECT SUMMARY The project will contribute MassIVE.quant, a novel data resource for quantitative mass spectrometry-based proteomics. Quantitative mass spectrometry characterizes proteins in complex biological mixtures with the highest available accuracy, sensitivity and throughput. Analysis of most such experiments involves identification of peptides and proteins that generated the spectra, and relative quantification of changes in abundance between pre-defined conditions. While the identifications workflows are now mature and ready for reproducible research, the quantitative workflows lag very far behind. No repositories can now store the analyses results across all workflows, and it is often impossible for authors to provide their data in a form that allows independent evaluation and reuse. This undermines the reproducibility and the impact of these investigations. The project combines the prior expertise of the Banderia’s lab in developing Mass spectrometry Interactive Virtual Environment (MassIVE), a public repository for storing, documenting and re-analyzing mass spectra for identification, and the prior expertise of the Vitek lab in developing MSstats, a broad-scope collection of statistical methods and software for quantitative proteomic workflows. First, the project will fully document and annotate a medium scale “training set” of quantitative investigations (which often rely on manual procedures), to develop standards for documenting and annotating the experiments with respect to the biological origins of the samples, and the technological aspects of data acquisition and processing. Second, the project will design functionalities for repository-wide complete and automated re-analyses of the original investigations, using a limited number of “good practice” workflows. The re-analyses will fully preserve the provenance of the results, and will be used to further characterize potential pitfalls in the experimental designs and conclusions. Finally, the project will place these investigations into a broader scientific context. It will design a query infrastructure that links each experiment to its peer investigations, i.e. investigations with similar biological or technological aspects, to provide insights into consistency of the results. Continuing the extensive prior outreach efforts of the PIs, the results will be disseminated to a broad community of stakeholders, including proteomic scientists, tool developers, journal editors, trainees, and scientists interested in protein-level information. The project will shift the mass spectrometry-based research paradigm, by creating a public resource that currently does not exist in any form. It will expand the technical capabilities of the field, ultimately allowing us to make more accurate of statements about the biological function.

项目总结 该项目将为基于定量质谱学的新型数据资源MassIVE.Quant做出贡献 蛋白质组学。 定量质谱学表征复杂生物混合物中的蛋白质 准确度、灵敏度和吞吐量。大多数这类实验的分析都涉及到多肽和 产生光谱的蛋白质，以及预先定义的丰度变化的相对量化 条件。虽然鉴定工作流程现在已经成熟，可以进行可重复的研究，但 量化工作流程远远落后于此。现在，任何资料档案库都不能存储所有 工作流，作者通常不可能以允许独立评估的形式提供他们的数据 和再利用。这破坏了这些调查的可重复性和影响。 该项目结合了Banderia实验室在开发MS Interactive方面的先前专业知识 虚拟环境(MASS)，一个公共存储库，用于存储、记录和重新分析 识别，以及Vitek实验室在开发MSstats方面的先前专业知识，这是一个广泛的统计集合 定量蛋白质组工作流程的方法和软件。首先，该项目将完整地记录和注释一个 中等规模的定量调查“训练集”(通常依靠人工程序)，以开发 关于样品的生物来源的实验记录和注释的标准， 以及数据采集和处理的技术方面。其次，该项目将设计功能 对于整个储存库范围的原始调查的完整和自动重新分析，使用有限数量的 “良好实践”工作流程。重新分析将完全保留结果的来源，并将用于 进一步描述实验设计和结论中的潜在陷阱。最后，该项目将把 这些调查涉及更广泛的科学背景。它将设计一个查询基础设施，将每个 对其同行调查进行实验，即具有类似生物学或技术方面的调查，以提供 对结果一致性的洞察。 继续督导人员先前广泛的外展工作，结果将向广大社区传播。 利益相关者，包括蛋白质组科学家、工具开发人员、期刊编辑、实习生和感兴趣的科学家 在蛋白质水平的信息中。 该项目将改变基于质谱学的研究范式，通过创建一个公共资源 目前不以任何形式存在。它将扩大该领域的技术能力，最终使我们能够 使有关生物功能的陈述更准确。